Land use classification from social media data and satellite imagery

• Published in: The Journal of supercomputing Vol. 76; no. 2; pp. 777 - 792
• Main Authors: Ye, Yaqin, An, Ying, Chen, Bo, Wang, JunJue, Zhong, Yingqiang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2020; Springer Nature B.V
• Subjects: Algorithms; Classification; Compilers; Computer Science; Digital media; Image classification; Interpreters; Land use; Land use management; Object recognition; Processor Architectures; Programming Languages; Satellite imagery; Social networks; Spatial data; Spatial distribution; Unevenness; Urban areas; Satellite image; Point of Interests; Land use; Classification
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-019-02922-6

Detailed urban land use classification plays a highly important role in the development and management of cities and in the identification of human activities. The complexity of the urban system makes its functional zoning a challenge, which makes such maps underutilized. A detailed land use classification encompasses both the natural land features and the classification of structures closely related to human activities. The use of satellite imagery to classify land use can effectively benefit the recognition of natural objects, but its performance demands significant improvement in the recognition of social functions due to the lack of information regarding human activities. To identify such activities in an urban area, we added Point of Interests (POI) data. This dataset contains both geographical tags and attributes that describe human activities. However, it has an uneven spatial distribution, with gaps in coverage being readily apparent. This paper proposes a land use classification framework using satellite imagery and data from social media. The proposed method employs a kernel density estimation to handle the spatial unevenness of POI data. The solution of mixed programming of MPI and OpenMP was adopted to parallel the algorithm. The results are compared to data compiled manually by means of human interpretation. Considering the example of Wuhan city, results show that the overall accuracy of land use type classification is 86.2%, and the Kappa coefficient is 0.860. It is demonstrated that using both POI and satellite images, a detailed land use map can be created automatically with satisfactory robustness.